Silent Pauses and Speech Indices as Biomarkers for Primary Progressive Aphasia

Abstract

Background and Objectives: Recent studies highlight the importance of investigating biomarkers for diagnosing and classifying patients with primary progressive aphasia (PPA). Even though there is ongoing research on pathophysiological indices in this field, the use of behavioral variables, and especially speech-derived factors, has drawn little attention in the relevant literature. The present study aims to investigate the possible utility of speech-derived indices, particularly silent pauses, as biomarkers for primary progressive aphasia (PPA). Materials and Methods: We recruited 22 PPA patients and 17 healthy controls, from whom we obtained speech samples based on two elicitation tasks, i.e., cookie theft picture description (CTP) and the patients’ personal narration of the disease onset and course. Results: Four main indices were derived from these speech samples: speech rate, articulation rate, pause frequency, and pause duration. In order to investigate whether these indices could be used to discriminate between the four groups of participants (healthy individuals and the three patient subgroups corresponding to the three variants of PPA), we conducted three sets of analyses: a series of ANOVAs, two principal component analyses (PCAs), and two hierarchical cluster analyses (HCAs). The ANOVAs revealed significant differences between the four subgroups for all four variables, with the CTP results being more robust. The subsequent PCAs and HCAs were in accordance with the initial statistical comparisons, revealing that the speech-derived indices for CTP provided a clearer classification and were especially useful for distinguishing the non-fluent variant from healthy participants as well as from the two other PPA taxonomic categories. Conclusions: In sum, we argue that speech-derived indices, and especially silent pauses, could be used as complementary biomarkers to efficiently discriminate between PPA and healthy speakers, as well as between the three variants of the disease.

A Systematic Review of the Quantitative markers of speech and language of the Frontotemporal Degeneration Spectrum and their potential for cross-linguistic implementation

“…As is visible in Figure 4A, the speech rate was significantly reduced compared to controls in more than half the papers for all patient groups except ALS-FTD. However, the Greek patient populations with mxPPA, lvPPA, and svPPA had no significant reduction in speech rate (Karpathiou & Kambanaros, 2022; Potagas et al, 2022). Some English patient populations with lvPPA, svPPA, and bvFTD also had no significant differences in speech rate (Ash et al, 2013; Bird et al, 2000; Mack et al, 2015; Marcotte et al, 2017; Pressman et al, 2019; Rohrer & Warren, 2010; Sajjadi et al, 2012; Thompson, 2012; Wilson et al, 2010).…”

“…76 papers studying discourse in patients with FTD were included in the final review. 57 of these papers (75% of all papers) studied English-speaking patients, the remaining 19 papers studied patients speaking Spanish (Baque et al, 2022; Matias-Guiu et al, 2020, 2022), Czech (Daoudi et al, 2022; Rusz et al, 2015; Skrabal et al, 2020), Italian (Catricala et al, 2019; Silveri et al, 2014), French (Bouvier et al, 2021; Macoir et al, 2021), German (Hohlbaum et al, 2018; Staiger A., 2017), Dutch (Bruffaerts et al, 2022), Greek (Karpathiou & Kambanaros, 2022; Koukoulioti et al, 2018, 2020; Potagas et al, 2022), Hindi (Sachin et al, 2008), and Korean (Suh et al, 2010) (Figure 2a). Figure 2b shows the geographical representation of the published papers, with a paucity of languages from South America, Asia, and Africa.…”

Efficient Pause Extraction and Encode Strategy for Alzheimer’s Disease Detection Using Only Acoustic Features from Spontaneous Speech

“…Mignard et al [ 50 ] investigated fluency disorders in Parkinson’s patients by the pause ratio. Potagas et al [ 51 ] used speech rate, articulation rate, pause frequency, and pause duration as analytical indicators, and the results showed that silent pauses can be used as complementary biomarkers for PPA. Imre et al [ 52 ] conducted a study on the temporal speech characteristics of elderly patients with type 2 diabetes (T2DM).…”

Automatically measuring speech fluency in people with aphasia: first achievements using read-speech data

“…They provide objective, reliable measures of fluency at the speech production level that could be part of the multidimensional fluency profiles that are called for in the domain. The automatic estimates of speech rate and of the number of silent or filled pauses they rely on are also used for assessing fluency in PWA (Gordon & Clough, 2022;Wang et al, 2013), with some of these indicators also being used for classifying subtypes of aphasia (Ash et al, 2013;Fraser et al, 2014;Potagas et al, 2022). Moreover, as they only process speech signals in the temporal domain, the algorithms require very low computing resources, which make their execution on standard PCs very rapid.…”